The performance of deep learning-based dehazing networks is highly dependent on large and rich paired datasets. However, the collection of paired data consisting of hazy images and clear images in real-world environments is extremely difficult. As a result, existing studies mostly adopted synthetic datasets for training, which leads to insufficient generalization ability of the networks in complex real-world scenarios. Therefore, a CycleGAN-based unsupervised image dehazing network was proposed to solve the problems of color distortion, artifacts, and incomplete dehazing that were prone to occur during the dehazing process of CycleGAN. Firstly, a residual dense connection module was designed to construct the encoder-decoder architecture,while  incorporating spatial and channel residual attention modules.An attention fusion mechanism was introduced in the skip connection section to achieve deep optimization of feature extraction and screening processes.Secondly, a new loss function was designed to effectively balance the visual authenticity and defogging accuracy of generated images.Experimental results show that, compared with baseline methods, the peak signal-to-noise ratio (PSNR) of the dehazed images of the designed network on outdoor synthetic datasets is improved by 22.71%, and the structural similarity (SSIM) value is increased by 6.49%. The image quality is improved  after defogging, and the network can also generate visually realistic foggy images.

In order to improve the operating ability of the system under unbalanced conditions, harmonic suppression was carried out from the perspective of output voltage control. Firstly, for the neutral point clamped(NPC) three-level inverter, fractional order modeling was performed on the controlled object in different coordinate systems to analyze the manifestations of the fundamental and harmonic components in the output voltage at different coordinates, and control was carried out in a two-phase stationary coordinate system. Secondly, to address the issue of low resonance bandwidth in proportional resonance and proportional complex integrators, an improved fractional-order proportional complex integral control (FO-QPCI) algorithm was adopted to suppress the imbalance of output voltage. Finally, the three-level inverter with the rapid control prototype(MT RCP) produced by ModelingTech software as the core controller was  experimentally verified on the physical platform, and results show that the proposed algorithm has good control ability to output voltage under unbalanced conditions and good suppression effect to harmonics.

In order to study the factors affecting on the washing effect of chemical tankers, palm oil was selected as a typical non-volatile chemical cargo, stainless steel lined bulkhead chemical tanker was taken as a research object, while a physical experiment system was built and developed a reliable numerical simulation model was developed. The quantitative evaluation model of palm oil tank washing effect was obtained by analyzing the results.During the experiment, it was found that the water jet was mainly influencing factor in the initial stage, and the dissolution of tank washing water in the middle and later stage was the mainly influencing factor. The research results show that, the main factors affecting the tank washing effect are the length and diameter of the nozzle outlet, washing time, temperature of the tank washing water and dynamic pressure.  This work has theoretical reference value for related research and good guidance significance for the practice of chemical tanker washing.

Taking ship traffic flow as the research object,  a Gaussiana gated recurrent unit (Gaussiana GRU) model based on the Gaussian distribution assumption was proposed to predict the uncertainty distribution of ship traffic flow parameters. Furthermore, a joint probability prediction method for traffic flow density and speed was established by combining with the Copula function. Firstly, based on the characteristics of traffic flow,  a residual GRU structure was designed to enhance the feature extraction capability of GRU through deep stacking, and the Gaussian likelihood function was combined to estimate the probability density distribution of traffic flow. Secondly, in order to solve the "lag" problem of prediction in uncertainty prediction, a point value processing module was introduced to improve the stability and accuracy of model prediction. The joint probability model of traffic flow density and speed was established by using Gaussian Copula function, and the state of three waterways in the Fujiangsha water area were estimated by using sampling method. Experimental results show that compared with the existing models, this method performs well in both point value  and probability density prediction, and can quantify the uncertainty characteristics of ship traffic flow more accurately.

For the collaborative transportation demand between Ro-Ro trailer terminals and inland customers with multiple task types, a tractor scheduling optimization model with time windows, trailer capacity constraints, and considering the inherent correlation between task types and node access order was constructed, and a mixed integer programming model with the goal of minimizing total  transportation costs was established. An improved adaptive large neighborhood search algorithm was proposed to enhance the search efficiency of the destroy and repair operators through a two-stage feasible arc generation strategy and a dynamic weight update mechanism. Numerical experiments demonstrate the correctness of the model and the effectiveness of the algorithm. The experimental results show that the flexible node visit strategy can coordinate the tractor and trailer resources without violating time and capacity constraints, reducing the total transportation cost by 3.16% to 6.47%. Compared with the traditional separation mode, the Ro-Ro separation mode can reduce the total cost by 5.29% to 12.71%,  decreasing the number of tractors required for operation by improving resource utilization. This study can provide a decision-making reference for resource coordination and scheduling between Ro-Ro terminals and the hinterland.